AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 121

An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker.

Which solution will provide this functionality with the LEAST implementation effort?

Answer options

• A. Manually install the libraries in the SageMaker containers.
• B. Build a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker jobs and pipelines.
• C. Create a SageMaker notebook instance to host the jobs. Create an AWS Lambda function to install the libraries on the notebook instance when the notebook instance starts. Configure the SageMaker jobs and pipelines to run on the notebook instance.
• D. Run code for the libraries externally on Amazon EC2 instances. Store the results in Amazon S3. Import the results into the SageMaker jobs and pipelines.

Correct answer: B

Explanation

Option B is correct because building a custom Docker container with the required libraries and hosting it in Amazon ECR allows for seamless integration with SageMaker, minimizing implementation effort. The other options, while feasible, involve more complex setups: option A requires manual installation each time, option C adds unnecessary complexity with a notebook instance and Lambda, and option D complicates the process by requiring external EC2 instances and S3 storage.